Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/46243
Title: Sparse representation for human gait recognition
Authors: Zeng, Zinan
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2011
Abstract: Sparsity-based algorithms recently have received great interests from statistics, signal processing, machine learning as well as computer vision. In this master thesis, it discusses the sparse representation based algorithms for computer vision problem, including the independent sparse representation (ISR), locality-constraint coding, group sparse representation (GSR). Based on these existing algorithms, two new algorithms referred to as locality-constrain group sparse representation (LGSR) and multiple-kernel group sparse representation (MKGSR) are proposed. Comprehensive experiments for Human Gait Recognition (HGR) using USF HumanID Gait database show that the two newly proposed methods, LGSR and MKGSR respectively achieve the best Rank-1 and Rank-5 recognition accuracy.
URI: http://hdl.handle.net/10356/46243
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

Files in This Item:
File Description SizeFormat 
sceG0902720D.pdf
  Restricted Access
11.18 MBAdobe PDFView/Open

Page view(s) 5

294
checked on Sep 23, 2020

Download(s) 5

22
checked on Sep 23, 2020

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.